Annual data might give a different result, worth presenting it both ways because that helps highlight the importance of the life history differences To get confidence intervals could do some bootstrapping - samples some of the 30 years and calculates it and so on - Julin could help with the code
Options:
Basic subtraction as in Moran et al 2017 (garden site - home site)
Gower’s environmental distance metric as in Rutter and Fenster 2007
From PCAs “We calculated an overall environmental distance using PCA, with EDij corresponding to the Euclidian distance between i and j based on PCA1 & PCA2.” as in Moran et al 2017
library(raster)
## Loading required package: sp
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks raster::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks raster::select()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(conflicted)
conflicts_prefer(dplyr::select())
## [conflicted] Will prefer dplyr::select over any other package.
conflicts_prefer(dplyr::filter)
## [conflicted] Will prefer dplyr::filter over any other package.
library(cowplot)
library(boot)
library(broom)
get_legend<-function(myggplot){
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
} #legend function for grid_arrange
elev_three_palette <- c("#0043F0", "#C9727F", "#F5A540") #colors from Gremer et al 2019
elev_order <- c("High", "Mid", "Low")
davis_climate <- read_csv("../output/Climate/flint_climate_UCDpops.csv") %>%
filter(parent.pop=="UCD_Garden") %>%
filter(year>=2022, year<2024) %>%
filter(if_else(year==2022, month=="nov" | month=="dec",
month!="nov" & month !="dec")) %>%
mutate(tavg = (tmn + tmx)/2, t_diurnal = (tmx-tmn))
## Rows: 38675 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): parent.pop, elevation.group, month
## dbl (11): elev_m, Lat, Long, year, aet, cwd, pck, pet, ppt, tmn, tmx
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(davis_climate)
## # A tibble: 6 × 16
## parent.pop elevation.group elev_m Lat Long year month aet cwd pck
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 UCD_Garden Low 16 38.5 -122. 2022 dec 3.74 20.5 0
## 2 UCD_Garden Low 16 38.5 -122. 2022 nov 3.77 33.3 0
## 3 UCD_Garden Low 16 38.5 -122. 2023 apr 29.4 88.9 0
## 4 UCD_Garden Low 16 38.5 -122. 2023 aug 42.4 139. 0
## 5 UCD_Garden Low 16 38.5 -122. 2023 feb 8.57 29.9 0
## 6 UCD_Garden Low 16 38.5 -122. 2023 jan 5.42 21.7 0
## # ℹ 6 more variables: pet <dbl>, ppt <dbl>, tmn <dbl>, tmx <dbl>, tavg <dbl>,
## # t_diurnal <dbl>
summary(davis_climate)
## parent.pop elevation.group elev_m Lat
## Length:12 Length:12 Min. :16 Min. :38.53
## Class :character Class :character 1st Qu.:16 1st Qu.:38.53
## Mode :character Mode :character Median :16 Median :38.53
## Mean :16 Mean :38.53
## 3rd Qu.:16 3rd Qu.:38.53
## Max. :16 Max. :38.53
## Long year month aet
## Min. :-121.8 Min. :2022 Length:12 Min. : 3.740
## 1st Qu.:-121.8 1st Qu.:2023 Class :character 1st Qu.: 5.298
## Median :-121.8 Median :2023 Mode :character Median :19.155
## Mean :-121.8 Mean :2023 Mean :25.706
## 3rd Qu.:-121.8 3rd Qu.:2023 3rd Qu.:44.822
## Max. :-121.8 Max. :2023 Max. :60.480
## cwd pck pet ppt
## Min. : 20.46 Min. :0 Min. : 24.20 Min. : 0.000
## 1st Qu.: 32.48 1st Qu.:0 1st Qu.: 38.15 1st Qu.: 0.415
## Median : 85.09 Median :0 Median :102.25 Median : 15.850
## Mean : 79.91 Mean :0 Mean :105.62 Mean : 60.338
## 3rd Qu.:115.29 3rd Qu.:0 3rd Qu.:168.95 3rd Qu.: 83.745
## Max. :144.82 Max. :0 Max. :205.30 Max. :258.000
## tmn tmx tavg t_diurnal
## Min. : 2.360 Min. :11.62 Min. : 7.275 Min. : 8.69
## 1st Qu.: 4.497 1st Qu.:14.57 1st Qu.: 9.669 1st Qu.:11.51
## Median : 8.720 Median :23.41 Median :16.067 Median :14.70
## Mean : 8.487 Mean :22.48 Mean :15.484 Mean :13.99
## 3rd Qu.:12.342 3rd Qu.:28.16 3rd Qu.:20.253 3rd Qu.:15.75
## Max. :15.470 Max. :34.37 Max. :24.520 Max. :19.75
davis_climate_flint <- davis_climate %>% #get the means for the variables to compare to home sites
summarise(cwd_Davis=mean(cwd),ppt_Davis=mean(ppt), pck_Davis=mean(pck), tmn_Davis=mean(tmn), tmx_Davis=mean(tmx))
davis_climate_flint
## # A tibble: 1 × 5
## cwd_Davis ppt_Davis pck_Davis tmn_Davis tmx_Davis
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 79.9 60.3 0 8.49 22.5
davis_climate$month <- factor(davis_climate$month, levels = c("nov","dec","jan", "feb", "mar", "apr", "may", "jun", "jul", "aug", "sep", "oct"))
davis_climate %>%
ggplot(aes(x=month,y=cwd)) +
geom_point()
davis_climate %>%
ggplot(aes(x=month,y=tmx)) +
geom_point()
davis_climate %>%
ggplot(aes(x=month,y=tmn)) +
geom_point()
davis_climate %>%
ggplot(aes(x=month,y=ppt)) +
geom_point()
Calculating wettest, driest, warmest, and coldest months
davis_wettest_month <- davis_climate %>%
slice_max(ppt)
davis_driest_month <- davis_climate %>%
slice_min(ppt)
davis_warmest_month <- davis_climate %>%
slice_max(tavg)
davis_coldest_month <- davis_climate %>%
slice_min(tavg)
Bio 1, 2, 4, 7, 12, 15
bioclim_davis_calc <- davis_climate %>%
summarise(ann_tmean=mean(tavg), #Bio1 - Annual Mean Temperature
mean_diurnal_range=mean(t_diurnal), #Bio2 - Mean Diurnal Range
temp_seasonality=sd(tavg), #Bio4 - Temperature Seasonality
temp_ann_range=(max(tmx))-(min(tmn)), #bio7 - temp annual range
ann_ppt=sum(ppt), #bio12 - annual precip
ppt_seasonality=cv(ppt+1)) #bio15 - Precipitation Seasonality (+1 to avoid strange CVs for areas where mean rainfaill is < 1)
bioclim_davis_calc
## # A tibble: 1 × 6
## ann_tmean mean_diurnal_range temp_seasonality temp_ann_range ann_ppt
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 15.5 14.0 6.34 32.0 724.
## # ℹ 1 more variable: ppt_seasonality <dbl>
Bio 8(Q), 9(Q), 18(Q), 19(Q)
#bio8 = tmean_wettest_month
bio8_davis <- davis_wettest_month %>%
dplyr::select(tmean_wettest_month=tavg)
#bio9 = tmean_driest_month
bio9_davis <- davis_driest_month %>%
dplyr::select(tmean_driest_month=tavg)
bio8_9_davis <- bind_cols(bio8_davis, bio9_davis)
#bio18 = ppt_warmest_month
bio18_davis <- davis_warmest_month %>%
dplyr::select(ppt_warmest_month=ppt)
#bio19 = ppt_coldest_month
bio19_davis <- davis_wettest_month %>%
dplyr::select(ppt_coldest_month=ppt)
bio18_19_davis <- bind_cols(bio18_davis, bio19_davis)
all_periods_davis <- bind_cols(bio8_9_davis, bio18_19_davis)
Merge all bioclims
davis_bioclim_final <- bind_cols(bioclim_davis_calc, all_periods_davis) %>%
rename_with(~paste0(., "_Davis"), 1:10)
summary(davis_bioclim_final)
## ann_tmean_Davis mean_diurnal_range_Davis temp_seasonality_Davis
## Min. :15.48 Min. :14 Min. :6.339
## 1st Qu.:15.48 1st Qu.:14 1st Qu.:6.339
## Median :15.48 Median :14 Median :6.339
## Mean :15.48 Mean :14 Mean :6.339
## 3rd Qu.:15.48 3rd Qu.:14 3rd Qu.:6.339
## Max. :15.48 Max. :14 Max. :6.339
## temp_ann_range_Davis ann_ppt_Davis ppt_seasonality_Davis
## Min. :32.01 Min. :724.1 Min. :144.2
## 1st Qu.:32.01 1st Qu.:724.1 1st Qu.:144.2
## Median :32.01 Median :724.1 Median :144.2
## Mean :32.01 Mean :724.1 Mean :144.2
## 3rd Qu.:32.01 3rd Qu.:724.1 3rd Qu.:144.2
## Max. :32.01 Max. :724.1 Max. :144.2
## tmean_wettest_month_Davis tmean_driest_month_Davis ppt_warmest_month_Davis
## Min. :9.305 Min. :24.5 Min. :0.16
## 1st Qu.:9.305 1st Qu.:24.5 1st Qu.:0.16
## Median :9.305 Median :24.5 Median :0.16
## Mean :9.305 Mean :24.5 Mean :0.16
## 3rd Qu.:9.305 3rd Qu.:24.5 3rd Qu.:0.16
## Max. :9.305 Max. :24.5 Max. :0.16
## ppt_coldest_month_Davis
## Min. :258
## 1st Qu.:258
## Median :258
## Mean :258
## 3rd Qu.:258
## Max. :258
(1/P) * SUM ((absolute value(Ai - Bi)) / range(i)) for each variable
P = number of environmental variables = 14 (without CWD)
Ai = 30 year avg of that variable for the home site
Bi = Nov 2022-Oct 2023 avg of that variable for the Davis garden
Range(i) = maximum - minimum of that variable in the whole data set (across sites)
davis_climate_all <- bind_cols(davis_climate_flint, davis_bioclim_final)
davis_range_prep <- davis_climate_all %>%
mutate(parent.pop="Davis") %>%
rename_with(~str_remove(., "_Davis"), everything())
recent_clim_boot <- read_csv("../output/Climate/growthseason_yrly_avgs_Recent.csv")
## Rows: 690 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): parent.pop, elevation.group
## dbl (19): elev_m, Lat, Long, year, cwd, pck, ppt, tmn, tmx, ann_tmean, mean_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
recent_clim_boot_nest <- recent_clim_boot %>% nest(.by=year) #nest to prepare for bootstrapping
recent_clim_boot_nest
## # A tibble: 30 × 2
## year data
## <dbl> <list>
## 1 1994 <tibble [23 × 20]>
## 2 1995 <tibble [23 × 20]>
## 3 1996 <tibble [23 × 20]>
## 4 1997 <tibble [23 × 20]>
## 5 1998 <tibble [23 × 20]>
## 6 1999 <tibble [23 × 20]>
## 7 2000 <tibble [23 × 20]>
## 8 2001 <tibble [23 × 20]>
## 9 2002 <tibble [23 × 20]>
## 10 2003 <tibble [23 × 20]>
## # ℹ 20 more rows
historical_clim_boot <- read_csv("../output/Climate/growthseason_yrly_avgs_Historical.csv")
## Rows: 690 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): parent.pop, elevation.group
## dbl (19): elev_m, Lat, Long, year, cwd, pck, ppt, tmn, tmx, ann_tmean, mean_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
historical_clim_boot_nest <- historical_clim_boot %>% nest(.by=year)
historical_clim_boot_nest
## # A tibble: 30 × 2
## year data
## <dbl> <list>
## 1 1964 <tibble [23 × 20]>
## 2 1965 <tibble [23 × 20]>
## 3 1966 <tibble [23 × 20]>
## 4 1967 <tibble [23 × 20]>
## 5 1968 <tibble [23 × 20]>
## 6 1969 <tibble [23 × 20]>
## 7 1970 <tibble [23 × 20]>
## 8 1971 <tibble [23 × 20]>
## 9 1972 <tibble [23 × 20]>
## 10 1973 <tibble [23 × 20]>
## # ℹ 20 more rows
#data <- recent_clim_boot_nest
#P=14
gowers_calc <- function(data, indices, P) { #function with all of the code necessary for calculating gowers distance
#data = _clim_boot (recent or historical) - needs to be nested by year; P = # climate variables
#need to make davis_range_prep before running this function
data <-data[indices,] # subset per bootstrap indices
data <- data %>% unnest(data) #unnest so the function can access the climate data
data_means <- data %>%
group_by(parent.pop, elevation.group, elev_m, Lat, Long) %>%
summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx", "ann_tmean", "mean_diurnal_range",
"temp_seasonality", "temp_ann_range",
"tmean_wettest_month", "tmean_driest_month", "ann_ppt",
"ppt_seasonality","ppt_warmest_month", "ppt_coldest_month"),
c(mean), na.rm = TRUE) #get 30 year averages for each climate variable
range_merge <- bind_rows(data_means, davis_range_prep)
davis_home_climate_ranges <- range_merge %>% #calculate ranges
ungroup() %>%
summarise(cwd_range=max(cwd)-min(cwd),
ppt_range=max(ppt)-min(ppt),
tmn_range=max(tmn)-min(tmn),
tmx_range=max(tmx)-min(tmx),
ann_tmean_range=max(ann_tmean)-min(ann_tmean),
mean_diurnal_range_range=max(mean_diurnal_range)-min(mean_diurnal_range),
temp_seasonality_range=max(temp_seasonality)-min(temp_seasonality),
temp_ann_range_range=max(temp_ann_range)-min(temp_ann_range),
tmean_wettest_month_range=max(tmean_wettest_month)-min(tmean_wettest_month),
tmean_driest_month_range=max(tmean_driest_month)-min(tmean_driest_month),
ann_ppt_range=max(ann_ppt)-min(ann_ppt),
ppt_seasonality_range=max(ppt_seasonality)-min(ppt_seasonality),
ppt_warmest_month_range=max(ppt_warmest_month)-min(ppt_warmest_month),
ppt_coldest_month_range=max(ppt_coldest_month)-min(ppt_coldest_month))
davis_home_climate <- bind_cols(davis_climate_all, data_means) #add davis climate data to home climate data
davis_home_climate_with_ranges <- bind_cols(davis_home_climate, davis_home_climate_ranges) #add in ranges
gowers_calc_each_var <- davis_home_climate_with_ranges %>% #variable by variable calc
mutate(cwd_gowers=abs(cwd_Davis-cwd) / cwd_range,
ppt_gowers=abs(ppt_Davis - ppt) / ppt_range,
tmn_gowers=abs(tmn_Davis - tmn) / tmn_range,
tmx_gowers=abs(tmx_Davis - tmx) / tmx_range,
ann_tmean_gowers=abs(ann_tmean_Davis - ann_tmean) / ann_tmean_range,
mean_diurnal_range_gowers=abs(mean_diurnal_range_Davis - mean_diurnal_range) / mean_diurnal_range_range,
temp_seasonality_gowers=abs(temp_seasonality_Davis - temp_seasonality) / temp_seasonality_range,
temp_ann_range_gowers=abs(temp_ann_range_Davis - temp_ann_range) / temp_ann_range_range,
tmean_wettest_month_gowers=abs(tmean_wettest_month_Davis - tmean_wettest_month) / tmean_wettest_month_range,
tmean_driest_month_gowers=abs(tmean_driest_month_Davis - tmean_driest_month) / tmean_driest_month_range,
ann_ppt_gowers=abs(ann_ppt_Davis - ann_ppt) / ann_ppt_range,
ppt_seasonality_gowers=abs(ppt_seasonality_Davis - ppt_seasonality) / ppt_seasonality_range,
ppt_warmest_month_gowers=abs(ppt_warmest_month_Davis - ppt_warmest_month) / ppt_warmest_month_range,
ppt_coldest_month_gowers=abs(ppt_coldest_month_Davis - ppt_coldest_month) / ppt_coldest_month_range) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_gowers"))
gowers_calc_per_pop <- gowers_calc_each_var %>% #final gowers calc
mutate(Gowers_Dist=(1/P)*(cwd_gowers + ppt_gowers + tmn_gowers + tmx_gowers +
ann_tmean_gowers + mean_diurnal_range_gowers +
temp_seasonality_gowers +temp_ann_range_gowers +
tmean_wettest_month_gowers +
tmean_driest_month_gowers +ann_ppt_gowers +
ppt_seasonality_gowers + ppt_warmest_month_gowers +
ppt_coldest_month_gowers)) %>%
dplyr::select(parent.pop, elevation.group, elev_m, Gowers_Dist)
gowers_calc_per_pop %>% pull(Gowers_Dist) #make the result a vector
}
#gowers_calc(recent_clim_boot_nest, P=14) #the function works
Recent
gowers.boot_recent <- boot(data=recent_clim_boot_nest, statistic=gowers_calc, R=1000, P=14) #will sample each row (year) with replacement
gowers.boot_recent$t0 #looks correct
## [1] 0.3303202 0.2274032 0.4220350 0.4838182 0.2356113 0.1701746 0.2289458
## [8] 0.4780364 0.4943449 0.4878916 0.4023189 0.3170032 0.3083460 0.3827274
## [15] 0.5458020 0.1931940 0.3331949 0.2070007 0.2198339 0.6131117 0.3366119
## [22] 0.4596776 0.4667175
#str(gowers.boot_recent)
for(i in 1:23) {
plot(gowers.boot_recent, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_recent, type="norm", index = 1) # for the first pop
boot_recent_results <- tidy(gowers.boot_recent,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Recent")
Historical
gowers.boot_historical <- boot(data=historical_clim_boot_nest, statistic=gowers_calc, R=1000, P=14) #will sample each row (year) with replacement
gowers.boot_historical$t0 #looks correct
## [1] 0.3355416 0.3989703 0.4044148 0.4669527 0.1666015 0.2094856 0.2526664
## [8] 0.6598801 0.6724588 0.6716469 0.3614024 0.3434374 0.3540532 0.4572285
## [15] 0.4695885 0.2417902 0.3710235 0.1763864 0.2514235 0.6186303 0.3662882
## [22] 0.4657659 0.4846714
#str(gowers.boot_historical)
for(i in 1:23) {
plot(gowers.boot_historical, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_historical, type="norm", index = 1) # for the first pop
boot_historical_results <- tidy(gowers.boot_historical,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Historical")
pops <- recent_clim_boot %>% select(parent.pop, elevation.group, elev_m, Lat, Long) %>% unique()
boot_gowers_recent_pops <- bind_cols(pops, boot_recent_results) %>% arrange(Gowers_Dist)
boot_gowers_historical_pops <- bind_cols(pops, boot_historical_results) %>% arrange(Gowers_Dist)
boot_gowers_results_all <- bind_rows(boot_gowers_recent_pops, boot_gowers_historical_pops)
write_csv(boot_gowers_results_all, "../output/Climate/growthseason_GowersEnvtalDist_UCD.csv")
recent_fig <- boot_gowers_results_all %>%
filter(TimePd=="Recent") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from UCD", fill="Elevation (m)", x="Population", title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
historical_fig <- boot_gowers_results_all %>%
filter(TimePd=="Historical") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from UCD", fill="Elevation (m)", x="Population", title="Historic Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
plot_grid(historical_fig, recent_fig)
ggsave("../output/Climate/growthseason_Gowers_fromDavis.png", width = 24, height = 8, units = "in")
#need to modify the gowers_calc function to only include Flint variables
gowers_calc_flint <- function(data, indices, P) { #function with all of the code necessary for calculating gowers distance
#data = _clim_boot (recent or historical) - needs to be nested by year; P = # climate variables
#need to make davis_range_prep before running this function
data <-data[indices,] # subset per bootstrap indices
data <- data %>% unnest(data) #unnest so the function can access the climate data
data_means <- data %>%
group_by(parent.pop, elevation.group, elev_m, Lat, Long) %>%
summarise_at(c("cwd", "pck", "ppt", "tmn", "tmx"),
c(mean), na.rm = TRUE) #get 30 year averages for each climate variable
range_merge <- bind_rows(data_means, davis_range_prep)
davis_home_climate_ranges <- range_merge %>% #calculate ranges
ungroup() %>%
summarise(cwd_range=max(cwd)-min(cwd),
ppt_range=max(ppt)-min(ppt),
tmn_range=max(tmn)-min(tmn),
tmx_range=max(tmx)-min(tmx))
davis_home_climate <- bind_cols(davis_climate_all, data_means) #add davis climate data to home climate data
davis_home_climate_with_ranges <- bind_cols(davis_home_climate, davis_home_climate_ranges) #add in ranges
gowers_calc_each_var <- davis_home_climate_with_ranges %>% #variable by variable calc
mutate(cwd_gowers=abs(cwd_Davis-cwd) / cwd_range,
ppt_gowers=abs(ppt_Davis - ppt) / ppt_range,
tmn_gowers=abs(tmn_Davis - tmn) / tmn_range,
tmx_gowers=abs(tmx_Davis - tmx) / tmx_range)
gowers_calc_per_pop <- gowers_calc_each_var %>% #final gowers calc
mutate(Gowers_Dist=(1/P)*(cwd_gowers + ppt_gowers + tmn_gowers + tmx_gowers)) %>%
dplyr::select(parent.pop, elevation.group, elev_m, Gowers_Dist)
gowers_calc_per_pop %>% pull(Gowers_Dist) #make the result a vector
}
recent_clim_boot_flint_nest <- recent_clim_boot %>% select(parent.pop:tmx) %>% nest(.by=year)
gowers.boot_flint_recent <- boot(data=recent_clim_boot_flint_nest, statistic=gowers_calc_flint, R=1000, P=4) #will sample each row (year) with replacement
gowers.boot_flint_recent$t0 #looks correct
## [1] 0.31824913 0.29331510 0.40839550 0.51912259 0.33581834 0.09738751
## [7] 0.31851299 0.49395909 0.54274463 0.48211356 0.42839910 0.25758761
## [13] 0.24239501 0.33255736 0.54633707 0.18522542 0.32604282 0.23955850
## [19] 0.17070224 0.52678833 0.30489544 0.52376981 0.49531896
#str(gowers.boot_flint_recent)
for(i in 1:23) {
plot(gowers.boot_flint_recent, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_flint_recent, type="norm", index = 1) # for the first pop
boot_flint_recent_results <- tidy(gowers.boot_flint_recent,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Recent")
historical_clim_boot_flint_nest <- historical_clim_boot %>% select(parent.pop:tmx) %>% nest(.by=year)
gowers.boot_flint_historical <- boot(data=historical_clim_boot_flint_nest, statistic=gowers_calc_flint, R=1000, P=4) #will sample each row (year) with replacement
gowers.boot_flint_historical$t0 #looks correct
## [1] 0.3289436 0.4559552 0.2702196 0.3827481 0.2964513 0.1134720 0.3499696
## [8] 0.7676875 0.8136684 0.7711139 0.3995840 0.2625098 0.3061110 0.4124108
## [15] 0.5638474 0.2384483 0.3944671 0.2498461 0.1616857 0.5225986 0.3555591
## [22] 0.3819858 0.3869474
#str(gowers.boot_flint_historical)
for(i in 1:23) {
plot(gowers.boot_flint_historical, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_flint_historical, type="norm", index = 1) # for the first pop
boot_flint_historical_results <- tidy(gowers.boot_flint_historical,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Historical")
boot_flint_gowers_recent_pops <- bind_cols(pops, boot_flint_recent_results) %>% arrange(Gowers_Dist)
boot_flint_gowers_historical_pops <- bind_cols(pops, boot_flint_historical_results) %>% arrange(Gowers_Dist)
boot_flint_gowers_results_all <- bind_rows(boot_flint_gowers_recent_pops, boot_flint_gowers_historical_pops)
write_csv(boot_flint_gowers_results_all, "../output/Climate/growthseason_GowersEnvtalDist_UCDFlint.csv")
recent_fig <- boot_flint_gowers_results_all %>%
filter(TimePd=="Recent") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from UCD", fill="Elevation (m)", x="Population", title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
historical_fig <- boot_flint_gowers_results_all %>%
filter(TimePd=="Historical") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from UCD", fill="Elevation (m)", x="Population", title="Historic Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
plot_grid(historical_fig, recent_fig)
ggsave("../output/Climate/growthseason_Gowers_Flint_fromDavis.png", width = 24, height = 8, units = "in")
pops_flint_avgs <- read_csv("../output/Climate/growthseason_FlintAvgs.csv")
## Rows: 46 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): parent.pop, elevation.group, TimePd
## dbl (7): elev_m, Lat, Long, cwd, ppt, tmn, tmx
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pops_flint_recent_avgs <- pops_flint_avgs %>% filter(TimePd=="Recent")
recent_flint_dist_prep <- bind_cols(davis_climate_flint, pops_flint_recent_avgs)
names(recent_flint_dist_prep)
## [1] "cwd_Davis" "ppt_Davis" "pck_Davis" "tmn_Davis"
## [5] "tmx_Davis" "parent.pop" "elevation.group" "elev_m"
## [9] "Lat" "Long" "cwd" "ppt"
## [13] "tmn" "tmx" "TimePd"
recent_flint_dist <- recent_flint_dist_prep %>%
mutate(cwd_dist=cwd_Davis - cwd,
ppt_dist=ppt_Davis - ppt,
tmn_dist=tmn_Davis - tmn,
tmx_dist=tmx_Davis - tmx) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
pops_flint_historic_avgs <- pops_flint_avgs %>% filter(TimePd=="Historical")
historic_flint_dist_prep <- bind_cols(davis_climate_flint, pops_flint_historic_avgs)
names(historic_flint_dist_prep)
## [1] "cwd_Davis" "ppt_Davis" "pck_Davis" "tmn_Davis"
## [5] "tmx_Davis" "parent.pop" "elevation.group" "elev_m"
## [9] "Lat" "Long" "cwd" "ppt"
## [13] "tmn" "tmx" "TimePd"
historic_flint_dist <- historic_flint_dist_prep %>%
mutate(cwd_dist=cwd_Davis - cwd,
ppt_dist=ppt_Davis - ppt,
tmn_dist=tmn_Davis - tmn,
tmx_dist=tmx_Davis - tmx) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
Figures Recent (subtraction distance)
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, cwd_dist), y=cwd_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanCWD_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_dist), y=ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanPPT_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmn_dist), y=tmn_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanTMN_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmx_dist), y=tmx_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanTMX_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
Figures Historical (subtraction distance)
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, cwd_dist), y=cwd_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanCWD_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_dist), y=ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanPPT_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmn_dist), y=tmn_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanTMN_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historic_flint_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmx_dist), y=tmx_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_MeanTMX_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
#need to modify the gowers_calc function to only include bioclim variables
gowers_calc_bioclim <- function(data, indices, P) { #function with all of the code necessary for calculating gowers distance
#data = _clim_boot (recent or historical) - needs to be nested by year; P = # climate variables
#need to make davis_range_prep before running this function
data <-data[indices,] # subset per bootstrap indices
data <- data %>% unnest(data) #unnest so the function can access the climate data
data_means <- data %>%
group_by(parent.pop, elevation.group, elev_m, Lat, Long) %>%
summarise_at(c("ann_tmean", "mean_diurnal_range",
"temp_seasonality", "temp_ann_range",
"tmean_wettest_month", "tmean_driest_month", "ann_ppt",
"ppt_seasonality","ppt_warmest_month", "ppt_coldest_month"),
c(mean), na.rm = TRUE) #get 30 year averages for each climate variable
range_merge <- bind_rows(data_means, davis_range_prep)
davis_home_climate_ranges <- range_merge %>% #calculate ranges
ungroup() %>%
summarise(ann_tmean_range=max(ann_tmean)-min(ann_tmean),
mean_diurnal_range_range=max(mean_diurnal_range)-min(mean_diurnal_range),
temp_seasonality_range=max(temp_seasonality)-min(temp_seasonality),
temp_ann_range_range=max(temp_ann_range)-min(temp_ann_range),
tmean_wettest_month_range=max(tmean_wettest_month)-min(tmean_wettest_month),
tmean_driest_month_range=max(tmean_driest_month)-min(tmean_driest_month),
ann_ppt_range=max(ann_ppt)-min(ann_ppt),
ppt_seasonality_range=max(ppt_seasonality)-min(ppt_seasonality),
ppt_warmest_month_range=max(ppt_warmest_month)-min(ppt_warmest_month),
ppt_coldest_month_range=max(ppt_coldest_month)-min(ppt_coldest_month))
davis_home_climate <- bind_cols(davis_climate_all, data_means) #add davis climate data to home climate data
davis_home_climate_with_ranges <- bind_cols(davis_home_climate, davis_home_climate_ranges) #add in ranges
gowers_calc_each_var <- davis_home_climate_with_ranges %>% #variable by variable calc
mutate(ann_tmean_gowers=abs(ann_tmean_Davis - ann_tmean) / ann_tmean_range,
mean_diurnal_range_gowers=abs(mean_diurnal_range_Davis - mean_diurnal_range) / mean_diurnal_range_range,
temp_seasonality_gowers=abs(temp_seasonality_Davis - temp_seasonality) / temp_seasonality_range,
temp_ann_range_gowers=abs(temp_ann_range_Davis - temp_ann_range) / temp_ann_range_range,
tmean_wettest_month_gowers=abs(tmean_wettest_month_Davis - tmean_wettest_month) / tmean_wettest_month_range,
tmean_driest_month_gowers=abs(tmean_driest_month_Davis - tmean_driest_month) / tmean_driest_month_range,
ann_ppt_gowers=abs(ann_ppt_Davis - ann_ppt) / ann_ppt_range,
ppt_seasonality_gowers=abs(ppt_seasonality_Davis - ppt_seasonality) / ppt_seasonality_range,
ppt_warmest_month_gowers=abs(ppt_warmest_month_Davis - ppt_warmest_month) / ppt_warmest_month_range,
ppt_coldest_month_gowers=abs(ppt_coldest_month_Davis - ppt_coldest_month) / ppt_coldest_month_range) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_gowers"))
gowers_calc_per_pop <- gowers_calc_each_var %>% #final gowers calc
mutate(Gowers_Dist=(1/P)*(ann_tmean_gowers + mean_diurnal_range_gowers +
temp_seasonality_gowers +temp_ann_range_gowers +
tmean_wettest_month_gowers +
tmean_driest_month_gowers +ann_ppt_gowers +
ppt_seasonality_gowers + ppt_warmest_month_gowers +
ppt_coldest_month_gowers)) %>%
dplyr::select(parent.pop, elevation.group, elev_m, Gowers_Dist)
gowers_calc_per_pop %>% pull(Gowers_Dist) #make the result a vector
}
recent_clim_boot_bioclim_nest <- recent_clim_boot %>% select(parent.pop:year, ann_tmean:ppt_coldest_month) %>% nest(.by=year)
gowers.boot_bioclim_recent <- boot(data=recent_clim_boot_bioclim_nest, statistic=gowers_calc_bioclim, R=1000, P=10) #will sample each row (year) with replacement
gowers.boot_bioclim_recent$t0 #looks correct
## [1] 0.3351487 0.2010384 0.4274908 0.4696964 0.1955285 0.1992895 0.1931189
## [8] 0.4716673 0.4749850 0.4902028 0.3918869 0.3407694 0.3347263 0.4027955
## [15] 0.5455880 0.1963815 0.3360558 0.1939776 0.2394866 0.6476411 0.3492985
## [22] 0.4340408 0.4552770
#str(gowers.boot_bioclim_recent)
for(i in 1:23) {
plot(gowers.boot_bioclim_recent, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_bioclim_recent, type="norm", index = 1) # for the first pop
boot_bioclim_recent_results <- tidy(gowers.boot_bioclim_recent,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Recent")
historical_clim_boot_bioclim_nest <- historical_clim_boot %>% select(parent.pop:year, ann_tmean:ppt_coldest_month) %>% nest(.by=year)
gowers.boot_bioclim_historical <- boot(data=historical_clim_boot_bioclim_nest, statistic=gowers_calc_bioclim, R=1000, P=10) #will sample each row (year) with replacement
gowers.boot_bioclim_historical$t0 #looks correct
## [1] 0.3381808 0.3761763 0.4580929 0.5006345 0.1146616 0.2478911 0.2137451
## [8] 0.6167572 0.6159749 0.6318601 0.3461297 0.3758085 0.3732301 0.4751556
## [15] 0.4318850 0.2431269 0.3616461 0.1470025 0.2873186 0.6570430 0.3705798
## [22] 0.4992779 0.5237611
#str(gowers.boot_bioclim_historical)
for(i in 1:23) {
plot(gowers.boot_bioclim_historical, index=i) #distributions look normal for the most part
}
#boot.ci(gowers.boot_bioclim_historical, type="norm", index = 1) # for the first pop
boot_bioclim_historical_results <- tidy(gowers.boot_bioclim_historical,conf.int=TRUE,conf.method="norm") %>% #all pops
rename(Gowers_Dist = statistic) %>%
mutate(TimePd="Historical")
boot_bioclim_gowers_recent_pops <- bind_cols(pops, boot_bioclim_recent_results) %>% arrange(Gowers_Dist)
boot_bioclim_gowers_historical_pops <- bind_cols(pops, boot_bioclim_historical_results) %>% arrange(Gowers_Dist)
boot_bioclim_gowers_results_all <- bind_rows(boot_bioclim_gowers_recent_pops, boot_bioclim_gowers_historical_pops)
write_csv(boot_bioclim_gowers_results_all, "../output/Climate/growthseason_GowersEnvtalDist_UCDbioclim.csv")
recent_fig <- boot_bioclim_gowers_results_all %>%
filter(TimePd=="Recent") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from UCD", fill="Elevation (m)", x="Population", title = "Recent Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
historical_fig <- boot_bioclim_gowers_results_all %>%
filter(TimePd=="Historical") %>%
ggplot(aes(x=fct_reorder(parent.pop, Gowers_Dist), y=Gowers_Dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
geom_errorbar(aes(ymin=conf.low, ymax=conf.high),
width=.1, position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(y="Gowers Envtal Distance \n from UCD", fill="Elevation (m)", x="Population", title="Historic Climate") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
plot_grid(historical_fig, recent_fig)
ggsave("../output/Climate/growthseason_Gowers_BioClim_fromDavis.png", width = 24, height = 8, units = "in")
pops_bioclim_avgs <- read_csv("../output/Climate/growthseason_BioClimAvgs.csv")
## Rows: 46 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): parent.pop, elevation.group, TimePd
## dbl (11): elev_m, ann_tmean, mean_diurnal_range, temp_seasonality, temp_ann_...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Recent
pops_bioclim_recent_avgs <- pops_bioclim_avgs %>% filter(TimePd=="Recent")
recent_bioclim_dist_prep <- bind_cols(davis_bioclim_final, pops_bioclim_recent_avgs)
recent_bioclim_dist <- recent_bioclim_dist_prep %>%
mutate(ann_tmean_dist=ann_tmean_Davis - ann_tmean,
mean_diurnal_range_dist=mean_diurnal_range_Davis - mean_diurnal_range,
temp_seasonality_dist=temp_seasonality_Davis - temp_seasonality,
temp_ann_range_dist=temp_ann_range_Davis - temp_ann_range,
tmean_wettest_month_dist=tmean_wettest_month_Davis - tmean_wettest_month,
tmean_driest_month_dist=tmean_driest_month_Davis - tmean_driest_month,
ann_ppt_dist=ann_ppt_Davis - ann_ppt,
ppt_seasonality_dist=ppt_seasonality_Davis - ppt_seasonality,
ppt_warmest_month_dist=ppt_warmest_month_Davis - ppt_warmest_month,
ppt_coldest_month_dist=ppt_coldest_month_Davis - ppt_coldest_month) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
recent_bioclim_dist
## # A tibble: 23 × 13
## parent.pop elevation.group elev_m ann_tmean_dist mean_diurnal_range_dist
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 BH Low 511. 2.58 0.558
## 2 CC Low 313 0.706 1.56
## 3 CP2 High 2244. 5.25 1.42
## 4 CP3 High 2266. 5.94 1.62
## 5 DPR Mid 1019. -0.00542 1.14
## 6 FR Mid 787 0.344 -1.54
## 7 IH Low 454. 0.834 0.841
## 8 LV1 High 2593. 6.10 -0.0611
## 9 LV3 High 2354. 6.12 -0.117
## 10 LVTR1 High 2741. 6.33 -0.261
## # ℹ 13 more rows
## # ℹ 8 more variables: temp_seasonality_dist <dbl>, temp_ann_range_dist <dbl>,
## # tmean_wettest_month_dist <dbl>, tmean_driest_month_dist <dbl>,
## # ann_ppt_dist <dbl>, ppt_seasonality_dist <dbl>,
## # ppt_warmest_month_dist <dbl>, ppt_coldest_month_dist <dbl>
#Historical
pops_bioclim_historical_avgs <- pops_bioclim_avgs %>% filter(TimePd=="Historical")
historical_bioclim_dist_prep <- bind_cols(davis_bioclim_final, pops_bioclim_historical_avgs)
historical_bioclim_dist <- historical_bioclim_dist_prep %>%
mutate(ann_tmean_dist=ann_tmean_Davis - ann_tmean,
mean_diurnal_range_dist=mean_diurnal_range_Davis - mean_diurnal_range,
temp_seasonality_dist=temp_seasonality_Davis - temp_seasonality,
temp_ann_range_dist=temp_ann_range_Davis - temp_ann_range,
tmean_wettest_month_dist=tmean_wettest_month_Davis - tmean_wettest_month,
tmean_driest_month_dist=tmean_driest_month_Davis - tmean_driest_month,
ann_ppt_dist=ann_ppt_Davis - ann_ppt,
ppt_seasonality_dist=ppt_seasonality_Davis - ppt_seasonality,
ppt_warmest_month_dist=ppt_warmest_month_Davis - ppt_warmest_month,
ppt_coldest_month_dist=ppt_coldest_month_Davis - ppt_coldest_month) %>%
dplyr::select(parent.pop, elevation.group, elev_m, ends_with("_dist"))
historical_bioclim_dist
## # A tibble: 23 × 13
## parent.pop elevation.group elev_m ann_tmean_dist mean_diurnal_range_dist
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 BH Low 511. 3.52 0.276
## 2 CC Low 313 2.84 1.42
## 3 CP2 High 2244. 4.67 0.339
## 4 CP3 High 2266. 5.30 0.717
## 5 DPR Mid 1019. 0.368 -0.201
## 6 FR Mid 787 0.924 -2.82
## 7 IH Low 454. 1.52 0.214
## 8 LV1 High 2593. 9.12 -1.05
## 9 LV3 High 2354. 9.14 -1.10
## 10 LVTR1 High 2741. 9.30 -1.07
## # ℹ 13 more rows
## # ℹ 8 more variables: temp_seasonality_dist <dbl>, temp_ann_range_dist <dbl>,
## # tmean_wettest_month_dist <dbl>, tmean_driest_month_dist <dbl>,
## # ann_ppt_dist <dbl>, ppt_seasonality_dist <dbl>,
## # ppt_warmest_month_dist <dbl>, ppt_coldest_month_dist <dbl>
Figures Recent (subtraction distance)
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_tmean_dist), y=ann_tmean_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Ann_Tmean_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, mean_diurnal_range_dist), y=mean_diurnal_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Diurnal_Range_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_seasonality_dist), y=temp_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Seasonality_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_ann_range_dist), y=temp_ann_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Ann_Range_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_wettest_month_dist), y=tmean_wettest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Wet_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_driest_month_dist), y=tmean_driest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Dry_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_ppt_dist), y=ann_ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Ann_PPT_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_seasonality_dist), y=ppt_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_PPT_Seasonality_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_warmest_month_dist), y=ppt_warmest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_PPT_Warm_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
recent_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_coldest_month_dist), y=ppt_coldest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_PPT_Cold_DistfromDavis_RecentClim.png", width = 12, height = 6, units = "in")
Historical (subtraction distance)
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_tmean_dist), y=ann_tmean_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Ann_Tmean_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, mean_diurnal_range_dist), y=mean_diurnal_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Diurnal_Range_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_seasonality_dist), y=temp_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Seasonality_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, temp_ann_range_dist), y=temp_ann_range_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Ann_Range_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_wettest_month_dist), y=tmean_wettest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Wet_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, tmean_driest_month_dist), y=tmean_driest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Temp_Dry_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ann_ppt_dist), y=ann_ppt_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_Ann_PPT_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_seasonality_dist), y=ppt_seasonality_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_PPT_Seasonality_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_warmest_month_dist), y=ppt_warmest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_PPT_Warm_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")
historical_bioclim_dist %>%
ggplot(aes(x=fct_reorder(parent.pop, ppt_coldest_month_dist), y=ppt_coldest_month_dist, group=parent.pop, fill=elev_m)) +
geom_col(width = 0.7,position = position_dodge(0.75)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradient(low = "#F5A540", high = "#0043F0") +
labs(fill="Elevation (m)",x="Population") +
theme_classic() +
theme(text=element_text(size=25), axis.text.x = element_text(angle = 45, hjust = 1))
#ggsave("../output/Climate/grwssn_PPT_Cold_DistfromDavis_HistoricalClim.png", width = 12, height = 6, units = "in")